wehub-resource-sync
121 lines
4.7 KiB
Python
121 lines
4.7 KiB
Python
import numpy as np
|
|
from sklearn.linear_model import LogisticRegression
|
|
from sklearn.metrics import f1_score
|
|
|
|
|
|
if __name__ == "__main__":
|
|
venue_count = 133
|
|
author_count = 246678
|
|
experiment_times = 1
|
|
percent = 0.05
|
|
file = open(".../output_file_path/...")
|
|
file_1 = open(".../label 2/googlescholar.8area.venue.label.txt")
|
|
file_2 = open(".../label 2/googlescholar.8area.author.label.txt")
|
|
check_venue = {}
|
|
check_author = {}
|
|
for line in file_1:
|
|
venue_label = line.strip().split(" ")
|
|
check_venue[venue_label[0]] = int(venue_label[1])
|
|
for line in file_2:
|
|
author_label = line.strip().split(" ")
|
|
check_author[author_label[0]] = int(author_label[1])
|
|
venue_embed_dict = {}
|
|
author_embed_dict = {}
|
|
# collect embeddings separately in dictionary form
|
|
file.readline()
|
|
print("read line by line")
|
|
for line in file:
|
|
embed = line.strip().split(" ")
|
|
if embed[0] in check_venue:
|
|
venue_embed_dict[embed[0]] = []
|
|
for i in range(1, len(embed), 1):
|
|
venue_embed_dict[embed[0]].append(float(embed[i]))
|
|
if embed[0] in check_author:
|
|
author_embed_dict[embed[0]] = []
|
|
for j in range(1, len(embed), 1):
|
|
author_embed_dict[embed[0]].append(float(embed[j]))
|
|
# get venue embeddings
|
|
print("reading finished")
|
|
venues = list(venue_embed_dict.keys())
|
|
authors = list(author_embed_dict.keys())
|
|
macro_average_venue = 0
|
|
micro_average_venue = 0
|
|
macro_average_author = 0
|
|
micro_average_author = 0
|
|
for time in range(experiment_times):
|
|
print("one more time")
|
|
np.random.shuffle(venues)
|
|
np.random.shuffle(authors)
|
|
venue_embedding = np.array([])
|
|
author_embedding = np.array([])
|
|
print("collecting venue embeddings")
|
|
for venue in venues:
|
|
temp = np.array(venue_embed_dict[venue])
|
|
if len(venue_embedding) == 0:
|
|
venue_embedding = temp
|
|
else:
|
|
venue_embedding = np.vstack((venue_embedding, temp))
|
|
print("collecting author embeddings")
|
|
count = 0
|
|
for author in authors:
|
|
count += 1
|
|
# print("one more author " + str(count))
|
|
temp_1 = np.array(author_embed_dict[author])
|
|
if len(author_embedding) == 0:
|
|
author_embedding = temp_1
|
|
else:
|
|
author_embedding = np.vstack((author_embedding, temp_1))
|
|
# split data into training and testing
|
|
print("splitting")
|
|
venue_split = int(venue_count * percent)
|
|
venue_training = venue_embedding[:venue_split, :]
|
|
venue_testing = venue_embedding[venue_split:, :]
|
|
author_split = int(author_count * percent)
|
|
author_training = author_embedding[:author_split, :]
|
|
author_testing = author_embedding[author_split:, :]
|
|
# split label into training and testing
|
|
venue_label = []
|
|
venue_true = []
|
|
author_label = []
|
|
author_true = []
|
|
for i in range(len(venues)):
|
|
if i < venue_split:
|
|
venue_label.append(check_venue[venues[i]])
|
|
else:
|
|
venue_true.append(check_venue[venues[i]])
|
|
venue_label = np.array(venue_label)
|
|
venue_true = np.array(venue_true)
|
|
for j in range(len(authors)):
|
|
if j < author_split:
|
|
author_label.append(check_author[authors[j]])
|
|
else:
|
|
author_true.append(check_author[authors[j]])
|
|
author_label = np.array(author_label)
|
|
author_true = np.array(author_true)
|
|
file.close()
|
|
print("beging predicting")
|
|
clf_venue = LogisticRegression(
|
|
random_state=0, solver="lbfgs", multi_class="multinomial"
|
|
).fit(venue_training, venue_label)
|
|
y_pred_venue = clf_venue.predict(venue_testing)
|
|
clf_author = LogisticRegression(
|
|
random_state=0, solver="lbfgs", multi_class="multinomial"
|
|
).fit(author_training, author_label)
|
|
y_pred_author = clf_author.predict(author_testing)
|
|
macro_average_venue += f1_score(
|
|
venue_true, y_pred_venue, average="macro"
|
|
)
|
|
micro_average_venue += f1_score(
|
|
venue_true, y_pred_venue, average="micro"
|
|
)
|
|
macro_average_author += f1_score(
|
|
author_true, y_pred_author, average="macro"
|
|
)
|
|
micro_average_author += f1_score(
|
|
author_true, y_pred_author, average="micro"
|
|
)
|
|
print(macro_average_venue / float(experiment_times))
|
|
print(micro_average_venue / float(experiment_times))
|
|
print(macro_average_author / float(experiment_times))
|
|
print(micro_average_author / float(experiment_times))
|